Falls risk screening tools intended to reduce fall risk among independent community-dwelling older adults : A systematic review - Mei Fong, Ong; Lam Soh, Kim; Rosalia, Saimon; Wai Myint, Wai; Saloma, Pawi; Hasni Idayu, Saidi (John Wiley & Sons Australia, Ltd., 2022)
Aims: The aim of this study is to evaluate an evidence-based fall risk screening tool to predict the risk of falls suitable for independent community-dwelling older adults guided by the World Health Organization's International Classification of Functioning, Disability and Health (WHO-ICF) components, and to examine the reliability and validity of the fall risk screening tool to predict fall risks, and to examine the feasibility of tools among independent community-dwelling older adults. Methods: A systematic literature search guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was performed using the EBSCOHost® platform, ScienceDirect, Scopus and Google Scholar between July and August 2021. Studies from January 2010 to January 2021 were eligible for review. Nine articles were eligible and included in this systematic review. The risk of bias assessment used the National Institutes of Health quality assessment tool for observational cohort and cross-sectional studies. The WHO-ICF helped to guide the categorization of fall risk factors. Results: Seven screening tools adequately predicted fall risk among communitydwelling older adults. Six screening tools covered most of the components of the WHO-ICF, and three screening tools omitted the environmental factors. The modified 18-item Stay Independent Brochure demonstrated most of the predictive values in predicting fall risk. All tools are brief and easy to use in community or outpatient settings. Conclusion: The review explores the literature evaluating fall risk screening tools for nurses and other healthcare providers to assess fall risk among independent community-dwelling older adults. A fall risk screening tool consisting of risk factors alone might be able to predict fall risk. However, further refinements and validations of the tools before use are recommended.